The present invention, in some embodiments thereof, relates to messaging and, more specifically, but not exclusively, to methods and systems for profiling electronic messages.
The existing infrastructure and low cost of electronic communications has resulted in an explosion of transmitted information. Individuals are subject to an ever increasing volume of email, short message service (SMS), instant messaging and/or the like. For example, email users, especially those with broad interest or job scope, may receive hundreds of emails daily. A prominent example is newsletters dissemination which is used by many companies and organizations in order to distribute information, for example promotional content, to large-scale audiences. These include information such as News and Advertisement. These email are usually distributed to a very large distribution list (bulk email), using automated tools. Companies like Amazon™, Apple™, Best-Buy™ and the like use these emails to reach a large targeted audience.
All of these messages are sorted through in order to prioritize those communications that demand attention and eliminate those that have no value to the recipient. Additionally, messages need to be cataloged, categorized, or sorted so that they can be readily accessed at a later time. It is desirable to perform all of these tasks in an efficient manner.
Typical solutions for handling email include viewing inbound mail by priority; for example, by color coding inbox views based on the email sender. Email is often analyzed based on content and manually or automatically assigned tags, or attributes to better allow future reference. A user may often manually examine and pigeonhole email, assigning tags, or filing the email in named folders. Storing email can also be done by algorithm based on time, source, topic. Machine learning algorithms can study an email user's patterns and recommend information storage schemes, or inbound attention priority schemes. These suffer from various problems, for example, not all mail from a source may have the same connotations of urgency, topic, or importance. Manual methods for handling email are slow and effortful. While faster, and requiring less effort on the part of the user, automated analysis may fail when email correspondents are uninformed or overdramatic (e.g., when the email is written to dramatize a situation which is not dramatic, or encourage action which is unnecessary). Additionally, machine learning can reinforce poor patterns of information management, learning from the email user's errors as well as her successes. Furthermore, as users collaborate with their colleagues, it is often discovered that initial sorting, or attribute tagging may be wrong, for example, as the user comes to better understand an evolving situation.